The invention relates to the segmentation of medical images, and in particular to automatic colon segmentation.
Medical image segmentation is the process of classifying voxels in a data set according to tissue type/anatomical feature. Colon segmentation is thus fee process of identifying which voxels in a medical image data set correspond to a patient's colon.
Colorectal carcinomas are responsible for a large number of deaths. Accordingly, colorectal screening is a commonly performed procedure. The aim of colorectal screening is to provide a clinician with a view of a patient's colon so that he can identify any anomalies, e.g. colorectal polyps, which warrant further study or surgery.
One technique for colorectal screening is colonoscopy. Traditional colonoscopy involves the use of an endoscope to allow direct visual inspection of a patient's colon. The technique is relatively invasive and so can be uncomfortable for the patient, and requires relatively heavy sedation. Accordingly, an often preferred alternative is virtual colonoscopy (computer simulated colonoscopy). Virtual colonoscopy is a technique based on obtaining three-dimensional medical image data of a patient, for example using x-ray computed tomography (CT) scanning techniques. Image data are normally obtained after the patient has undergone colonic lavage and sufflattion so that the colon is clear and full of gas. The medical image data are then processed and a graphical reproduction of the sufflated colon displayed to a clinician. The clinician may then provide a diagnosis based on his analysis of the graphical reproduction. Virtual colonoscopy has the advantages of being less invasive for the patient and providing the clinician with much greater flexibility in the way he can view the virtual colon. For example, the clinician can choose to observe the virtual colon from almost any direction, from both inside and outside the colon, and can easily zoom-in and -out of any regions considered to be of particular interest.
A fundamental aspect of virtual colonoscopy is the process of identifying which voxels in the medical image data set correspond to the colon and which do not (i.e. colon segmentation). This is what allows voxels corresponding to the colonic wall to be rendered visible in images displayed to the clinician, while other “non-colon” voxels are rendered transparent.
The usual procedure for segmenting the colon is to first identify a so-called “seed” voxel within the colon lumen. The next step is to then “grow” a region around the seed voxel to identify connected voxels having similar characteristics (e.g. x-ray absorption density). The result of the growing process is thus a connected region of voxels having similar characteristics to the seed voxel. This connected region is taken to correspond to the colon. These methods for segmentation may be referred to as “seed-and-grow” methods.
There are various well known processes for growing a region around the seed voxel [1, 2]. These can be computationally intensive, but are otherwise generally relatively straightforward. However, the process of identifying an appropriate seed voxel in the first instance is not generally so simple.
A common way of identifying an appropriate seed is to provide the clinician with an overview image of the data set which is rendered in such a way that all anatomical features are apparent. The clinician is thus able to visually identify the colon in the image and identify a seed voxel within it. At this stage the clinician will also define an appropriate radiodensity threshold between the density of gas and the density of body tissue in the image data to define the boundary of the colon for the region growing procedure.
This approach is relatively reliable, but has the disadvantage of requiring clinician input. The required input can be significant because the colon will often not be apparent as a single connected region in the medical image data, e.g., because of obstructions in the colon such as peristalsis, very large legions or residual faeces. This means the clinician will often need to identify multiple seed voxels (i.e., one in each separated region of colon). In addition to being time more consuming, there is also an increase in the likelihood of an incomplete segmentation.
Schemes for automating colon segmentation procedures have therefore been proposed. Automation is generally preferable for medical image data pre-processing because it saves clinician time, both in terms of the clinician having to take part in the pre-processing itself, and also in terms of the clinician having to wait for the results of the pre-processing before continuing his analysis. Automation can also help to improve objectivity and reproducibility in the analysis because it removes a possible source of clinician subjectivity.
One previously proposed scheme for automatic colon segmentation is described by Wystt et at in “Automatic Segmentation of the Colon for Virtual Colonoscopy” [3]. Wyatt et al.'s technique is a seed-and-grow technique in which the seed voxel is determined automatically.
In a first stage of Wyatt et at's scheme, the voxels in a medical image data set which are internal to the patient and which correspond with air/gas are identified. This is done by assuming only those voxels with a radiodensity below −800 HU are air/gas. Voxels with a radiodensity above −800 HU, on the other hand, are taken to represent body tissue. Voxels comprising the air surrounding the patient are identified by a series of seed-and-grow procedures seeded by voxels at the corners of the data set. Connected regions of voxels having radiodensity below −800 HU and including one of the eight corners of the volume data set are taken to correspond with surrounding air.
In a second stage, the distance from each of the internal air/gas voxels to its nearest voxel comprising body tissue is determined. This is done by applying a 3-4-5 chamfer distance transform based on a two-pass recursive morphology operation. The internal air/gas voxel having the greatest distance from surrounding body tissue is necessarily near the centre of the largest gas/air filled volume in the patient. When distended by insufflation, the colon is generally the largest gas-filled structure in the lower abdomen. Accordingly, the internal air/gas voxel having the greatest distance from surrounding body tissue is taken as the seed voxel for the colon. The region grown from this seed is taken to correspond to the colon.
In a third stage of Wyatt et al.'s scheme, the elongation of the connected region obtained in the seed-and-grow procedure from the automatically selected seed voxel is characterised. This is done because the stomach can sometimes present the largest gas/air filled volume in the patient. However, whereas the upper-most sections of the colon are relatively elongate, the stomach, which is normally in the same portion of the volume data as the upper-most sections of the colon, is usually less so. Thus if the connected region is in the region of the volume data that would normally include the stomach and is deemed not to be sufficiently elongate, it is determined that the connected region corresponds to the stomach and not the colon. The voxels in this connected region are thus discounted from the segmentation and the second stage is repeated.
A problem with this scheme is that the seed-and-grow procedure is computation intensive. Furthermore, multiple seed-and-grow procedures are generally required, for example to properly segment an obstructed colon, and also in those cases where the stomach is identified. By way of example, Wyatt et al. report run-times on the order of one hour for the algorithm to process a single volume data set using an SGI Onyx computer with a MIPS R10000 processor.
Another previously proposed scheme for automatic colon segmentation is described by Frimmel et al. in “Centerline-based Colon Segmentation for CT Colonography” [4]. The scheme relies on calculating local centerpoints along thresholded components of abdominal air, and connecting the centerpoints iteratively to yield centerlines. Anatomy based metrics are then used to identify centerlines not associated with the colon (e.g. because the centerlines follow shapes which are not characteristic of the colon). A thick region encompassing the colonic wall is extracted by region-growing around the remaining centerline.
Like Wyatt et al.'s scheme, this process is in effect another seed-and-grow method in which appropriate seed voxels are determined automatically. In this case the seed voxels are selected by virtue of being located on a centerline determined to run through the colon based on its characteristic shape. This method is again relatively computationally intensive, typically taking on the order of tens of seconds to process a data set.
There is therefore a need for a simple, reliable and robust method and apparatus for automatic colon segmentation.